1. Field of the Invention
The present invention relates to a machine learning method and machine learning apparatus learning an operating command to an electric motor and a controller and an electric motor apparatus including that machine learning apparatus.
2. Description of the Related Art
To efficiently perform a desired process utilizing an electric motor, shortening the cycle time is desirable. It is possible to optimize the acceleration or deceleration (below, sometimes referred to as “acceleration/deceleration”) of an electric motor so as to shorten the cycle time. In general, the acceleration/deceleration of an electric motor is designated by the operator, so to realize optimization, the knowledge and experience of the operator were relied on to a large extent. Trial and error was also essential.
Another factor making optimization of acceleration/deceleration difficult is the heat generated from a controller controlling the electric motor. An electric motor is controlled so as to operate in a range where no overheating occurs, but whether overheating occurs differs depending on the ambient environment of the electric motor. Therefore, to reliably prevent overheating from occurring, it is necessary to assume the harshest ambient environment when optimizing acceleration/deceleration. As a result, when the ambient temperature is low, the operation of the electric motor is not actually optimized and the cycle time tends to increase.
In is known to adjust the output of an electric motor in accordance with the temperature of the heat generating source. For example, it is known to estimate the temperature of a power semiconductor module for driving an electric motor and limit the output of the electric motor when the estimated temperature exceeds the reference temperature (see Japanese Patent Publication No. 2014-239631A).